scholarly journals Solar Irradiance Assessment and Forecasting in Tropical Climates using Satellite Remote Sensing and Physical Modelling

2021 ◽  
Author(s):  
Akriti Masoom ◽  
Panagiotis Kosmopolous ◽  
Ankit Bansal
Keyword(s):  
Solar Energy ◽  
Optical Depth ◽  
Solar Irradiance ◽  
Distribution System ◽  
Wrf Model ◽  
Physical Modelling ◽  
Final Analysis ◽  
Surface Radiation ◽  
Atmospheric Composition ◽  
Energy Potential

<p>Poor resolution of solar irradiance ground data demonstrates the necessity and provides an opportunity for satellite data-based solar irradiance modeling. The study is focused on India due to its tropical climate that provides varied as well as extreme conditions for solar energy research. For solar energy monitoring in near real-time, the Indian Solar Irradiance Operational System (INSIOS) was developed using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate, respectively. It had high accuracy under clear-sky conditions for global horizontal irradiance (GHI) and direct normal irradiance (DNI) that were evaluated for a year at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The presented methodology could effectively support the penetration of photovoltaic installation as estimations were reliable during high solar energy potential conditions with median BSRN and INSIOS difference varying from 93 to 49 W/m<sup>2</sup> for GHI.</p><p>Further, an operational day-ahead solar irradiance forecasting system (WRF-CAMS) is presented that ingests CAMS aerosol optical depth (AOD) into the WRF model to better quantify the aerosol impact on solar energy long-term forecasts, and validation was done against ground-based measurements from BSRN stations. The analysis was carried out for forecast horizons varying from 24 h to 36 h for different seasons, varying solar zenith angles, and different cloud cover classifications based on calculated clearness index. The correlation coefficient was improved from 0.93 to 0.95 for GHI and 0.75 to 0.82 for DNI after the ingestion of CAMS AOD as compared to WRF default aerosol scheme. The annual root mean square error was observed to vary from 10 to 130 W/m<sup>2</sup> and 50 to 190 W/m<sup>2</sup> for GHI and DNI, respectively. This system is hoped to provide more accurate forecasts for better solar plant energy planning and improve day-to-day electricity exchange market supporting solar energy producers and distribution system operators.</p><p>In the final analysis, INSIOS and WRF-CAMS models were used for forecasting dust impact on solar irradiance during an extreme dust event using Aeronet measurements, satellite observations (MODIS and CALIPSO), and ModIs Dust AeroSol (MIDAS) dust database. WRF-CAMS model was used to examine dust impact on solar irradiance for a high-intensity dust storm with AOD and dust optical depth values reaching up to 2. The observed average decrease in GHI and DNI due to the dust plume was 76 W/m<sup>2</sup> and 275 W/m<sup>2</sup>, respectively, and a maximum reduction of 100 W/m<sup>2</sup> (10%) and 400 W/m<sup>2</sup> (40%), respectively. The proposed methodology can support solar energy producers, for optimum energy production forecasting, management, and maintenance as well as transmission and distribution system operators, as dust events of this extent significantly reduce solar irradiance and affect energy exploitation capacity due to solar aerosol-related extinction.</p>

scholarly journals Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

2020 ◽  
Vol 12 (2) ◽  
pp. 254 ◽  
Author(s):  
Akriti Masoom ◽  
Panagiotis Kosmopoulos ◽  
Ankit Bansal ◽  
Stelios Kazadzis
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Urban Areas ◽  
Surface Radiation ◽  
Atmospheric Composition ◽  
Coefficient Of Determination ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Energy Potential ◽  
Potential Conditions

Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions.

Assessment of a newly developed short-term forecasting system (nextSENSE) of Downwelling Surface Solar Irradiance (DSSI) and validation with ground-based measurements

2021 ◽  
Author(s):  
Kyriakoula Papachristopoulou ◽  
Ilias Fountoulakis ◽  
Panagiotis Kosmopoulos ◽  
Dimitris Kouroutsidis ◽  
Panagiotis I. Raptis ◽  
...  
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Surface Radiation ◽  
Solar Irradiation ◽  
Short Term ◽  
Large Spatial Scale ◽  
Infrared Imager ◽  
Flow Motion ◽  
Forecasting System ◽  
Short Term Forecasting

<p>Monitoring and forecasting cloud coverage is crucial for nowcasting and forecasting of solar irradiance reaching the earth surface, and it’s a powerful tool for solar energy exploitation systems.</p><p>In this study, we focused on the assessment of a newly developed short-term (up to 3h) forecasting system of Downwelling Surface Solar Irradiation (DSSI) in a large spatial scale (Europe and North Africa). This system forecasts the future cloud position by calculating Cloud Motion Vectors (CMV) using Cloud Optical Thickness (COT) data derived from multispectral images from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite and an optical flow motion estimation technique from the computer vision community. Using as input consecutive COT images, CMVs are calculated and cloud propagation is performed by applying them to the latest COT image. Using the predicted COT images, forecasted DSSI is calculated using Fast Radiative Transfer Models (FRTM) in high spatial (5 km over nadir) and temporal resolution (15 min time intervals intervals).</p><p>A first evaluation of predicted COT has been conducted, by comparing the predicted cloud parameter of COT with real observed values derived by the MSG/SEVIRI. Here, the DSSI is validated against ground-based measurements from three Baseline Surface Radiation Network (BSRN) stations, for the year 2017. Also, a sensitivity analysis of the effect on DSSI for different cloud and aerosol conditions is performed, to ensure reliability under different sky and climatological conditions.</p><p>The DSSI short-term forecasting system proposed, complements the existing short-term forecasting techniques and it is suitable for operational deployment of solar energy related systems</p><p>Acknowledgements</p><p>This study was funded by the EuroGEO e-shape (grant agreement No 820852).</p>

scholarly journals Assessment of the solar energy potential in Greece using ground-based measurements, satellite observations and numerical simulations of radiative transfer models

2018 ◽  
Author(s):  
Παναγιώτης-Νεκτάριος Κοσμόπουλος
Keyword(s):  
Solar Energy ◽  
Radiative Transfer ◽  
Numerical Simulations ◽  
Satellite Observations ◽  
Surface Radiation ◽  
Energy Potential ◽  
Radiative Transfer Models ◽  
Transfer Models

Η παρούσα διδακτορική διατριβή πραγματεύεται την εκτίμηση του ηλιακού δυναμικού στον Ελλαδικό χώρο με επίγειες και δορυφορικές παρατηρήσεις και αριθμητικές προσομοιώσεις μοντέλων διάδοσης της ακτινοβολίας. Αρχικά χρησιμοποιήθηκαν πυρανόμετρα από το Ελληνικό Δίκτυο Ηλιακής Ενέργειας και το δίκτυο σταθμών του Εθνικού Αστεροσκοπείου Αθηνών με σκοπό την αξιολόγηση προγνώσεων ηλιακής ενέργειας 1 και 2 ημερών μπροστά από το μοντέλο MM5. Το ολικό δυναμικό ηλιακής ενέργειας βρέθηκε να κυμαίνεται μεταξύ 1,5 και 1,9 MWh/m2 με τη νέφωση να προκαλεί αύξηση των σφαλμάτων πρόγνωσης της τάξης του 10%. Εν συνεχεία, εκτιμήθηκε η επίδραση των αιωρούμενων σωματιδίων στην επιφανειακή ηλιακή ακτινοβολία, εστιάζοντας σε ένα έντονο επεισόδιο σκόνης, αναδεικνύοντας τις υψηλές απώλειες ηλιακής ενέργειας της τάξης του 80 και 50% για τα συγκεντρωτικά (CSP) και τα φωτοβολταϊκά (PV) συστήματα αντίστοιχα. Οι παραπάνω αναλύσεις πραγματικών σεναρίων και ατμοσφαιρικών συνθηκών συνεισφέρουν στην κατανόηση αλλά και στην ποσοτικοποίηση του εύρους της επίδρασης που έχουν τα νέφη και τα αιωρούμενα σωματίδια στην ηλιακή ενέργεια, αλλά και στις δυνητικές αστοχίες πρόγνωσης σε περιοχές με υψηλό ηλιακό δυναμικό όπως είναι η Ελλάδα. Ταυτόχρονα, αναπτύχθηκε μια σειρά από καινοτόμα μοντέλα εκτίμησης του δυναμικού ηλιακής ενέργειας τα οποία δύνανται να λειτουργούν επιχειρησιακά και σε πραγματικό χρόνο. Τα μοντέλα αυτά βασίζονται σε πίνακες προσομοιώσεων διάδοσης της ακτινοβολίας, σε τεχνικές υπολογιστικής επιτάχυνσης με νευρωνικά δίκτυα και εξισώσεις πολλαπλής παλινδρόμησης και δορυφορικά δεδομένα εισόδου πραγματικού χρόνου. Η αξιοπιστία τους επαληθεύτηκε με επίγειες μετρήσεις των δικτύων Baseline Surface Radiation Network και Global Atmosphere Watch από τη νότια Αφρική μέχρι τη βόρεια Ευρώπη, ενώ πραγματοποιήθηκε και μια ανάλυση ευαισθησίας των δεδομένων εισόδου (κυρίως νεφών και αιωρούμενων σωματιδίων) ως προς την επίδρασή τους στην ηλιακή ακτινοβολία. Η συνολική προσέγγιση του διδακτορικού στοχεύει στο να αποτελέσει ένα βήμα μπροστά στην πρόγνωση της ηλιακής ενέργειας, στον επιχειρησιακά εφικτό ενεργειακό σχεδιασμό των ηλιακών πάρκων, στην αποτελεσματική εκμετάλλευση της ηλιακής ενέργειας, αλλά και στο να συνεισφέρει στο σχηματισμό πολιτικών που αφορούν τις ανανεώσιμες πηγές ενέργειας.

scholarly journals The 2017 Mega-Fires in Central Chile: Impacts on Regional Atmospheric Composition and Meteorology Assessed from Satellite Data and Chemistry-Transport Modeling

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 344
Author(s):  
Rémy Lapere ◽  
Sylvain Mailler ◽  
Laurent Menut
Keyword(s):  
Optical Depth ◽  
Surface Radiation ◽  
Capital City ◽  
Transport Modeling ◽  
Atmospheric Composition ◽  
Atmospheric Effects ◽  
Central Chile ◽  
Fire Emissions ◽  
South Central ◽  
The Impact

In January 2017, historic forest fires occurred in south-central Chile. Although their causes and consequences on health and ecosystems were studied, little is known about their atmospheric effects. Based on chemistry-transport modeling with WRF-CHIMERE, the impact of the 2017 Chilean mega-fires on regional atmospheric composition, and the associated meteorological feedback, are investigated. Fire emissions are found to increase pollutants surface concentration in the capital city, Santiago, by +150% (+30 µg/m3) for PM2.5 and +50% (+200 ppb) for CO on average during the event. Satellite observations show an intense plume extending over 2000 km, well reproduced by the simulations, with Aerosol Optical Depth at 550 nm as high as 4 on average during the days of fire activity, as well as dense columns of CO and O3. In addition to affecting atmospheric composition, meteorology is also modified through aerosol direct and indirect effects, with a decrease in surface radiation by up to 100 W/m2 on average, leading to reductions in surface temperatures by 1 K and mixing layer heights over land by 100 m, and a significant increase in cloud optical depth along the plume. Large deposition fluxes of pollutants over land, the Pacific ocean and the Andes cordillera are found, signaling potential damages to remote ecosystems.

scholarly journals ANALISIS KALIBRASI SENSOR BH1750 UNTUK MENGUKUR RADIASI MATAHARI DI PEKANBARU

2018 ◽  
Author(s):  
Amanda Khaira Perdana ◽  
Iswadi Hasyim Rosma
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Energy Potential ◽  
Potential Measurement ◽  
Measurement Capability ◽  
Significant Parameter ◽  
Number Of Factors ◽  
Different Types ◽  
Solar Station

Solar irradiance is one of significant parameter to describe to available potential of solar energy in a particular location. Measuring solar irradiance can be implemented by using different types of sensors, namely: pyranometer, pyrheliometer, light dependent resistor, photodioda and phototransistor. However, when implementing these sensors for solar energy potential measurement, a number of factors must be considered such as: sensor’s price and measurement capability. Therefore, the aim of this article is to analyze the used of low price solar irradiance sensor as part of automatic solar station for measuring solar energy potential in a particular site. BH1750 was used in this article where it has been found that it has limitations such as maximum capability is up to 55.000 lux. A method was introduced to increase measurement capability by putting a cover on the sensor. With this additional cover, specific calibrations need to be carried out to overcome sensor’s accuracy.

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

scholarly journals Rooftop Solar PV Penetration Impacts on Distribution Network and Further Growth Factors—A Comprehensive Review

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Busra Uzum ◽  
Ahmet Onen ◽  
Hany M. Hasanien ◽  
S. M. Muyeen
Keyword(s):  
Solar Energy ◽  
Power Quality ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
System Stability ◽  
Solar Pv ◽  
Voltage Quality ◽  
Solution Methods ◽  
Pv Penetration

In order to meet the electricity needs of domestic or commercial buildings, solar energy is more attractive than other renewable energy sources in terms of its simplicity of installation, less dependence on the field and its economy. It is possible to extract solar energy from photovoltaic (PV) including rooftop, ground-mounted, and building integrated PV systems. Interest in rooftop PV system applications has increased in recent years due to simple installation and not occupying an external area. However, the negative effects of increased PV penetration on the distribution system are troublesome. The power loss, reverse power flow (RPF), voltage fluctuations, voltage unbalance, are causing voltage quality problems in the power network. On the other hand, variations in system frequency, power factor, and harmonics are affecting the power quality. The excessive PV penetration also the root cause of voltage stability and has an adverse effect on protection system. The aim of this article is to extensively examines the impacts of rooftop PV on distribution network and evaluate possible solution methods in terms of the voltage quality, power quality, system protection and system stability. Moreover, it is to present a comparison of the advantages/disadvantages of the solution methods discussed, and an examination of the solution methods in which artificial intelligence, deep learning and machine learning based optimization and techniques are discussed with common methods.

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